{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('AQI_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>AQI</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>main_pollution</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京市</td>\n",
       "      <td>61</td>\n",
       "      <td>14</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.3</td>\n",
       "      <td>61</td>\n",
       "      <td>PM10</td>\n",
       "      <td>2019-02-04 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>天津市</td>\n",
       "      <td>104</td>\n",
       "      <td>41</td>\n",
       "      <td>157</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>0.9</td>\n",
       "      <td>53</td>\n",
       "      <td>PM10</td>\n",
       "      <td>2019-02-04 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>石家庄市</td>\n",
       "      <td>125</td>\n",
       "      <td>36</td>\n",
       "      <td>200</td>\n",
       "      <td>12</td>\n",
       "      <td>47</td>\n",
       "      <td>0.9</td>\n",
       "      <td>30</td>\n",
       "      <td>PM10</td>\n",
       "      <td>2019-02-04 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>唐山市</td>\n",
       "      <td>66</td>\n",
       "      <td>42</td>\n",
       "      <td>81</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>PM10</td>\n",
       "      <td>2019-02-04 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>秦皇岛市</td>\n",
       "      <td>55</td>\n",
       "      <td>25</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0.9</td>\n",
       "      <td>48</td>\n",
       "      <td>PM10</td>\n",
       "      <td>2019-02-04 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   city  AQI PM2.5 PM10 SO2 NO2   CO  O3 main_pollution                 time\n",
       "0   北京市   61    14   71   3  12  0.3  61           PM10  2019-02-04 00:00:00\n",
       "1   天津市  104    41  157   8  21  0.9  53           PM10  2019-02-04 00:00:00\n",
       "2  石家庄市  125    36  200  12  47  0.9  30           PM10  2019-02-04 00:00:00\n",
       "3   唐山市   66    42   81  22  16  1.0  52           PM10  2019-02-04 00:00:00\n",
       "4  秦皇岛市   55    25   60  12  22  0.9  48           PM10  2019-02-04 00:00:00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 71825 entries, 0 to 71824\n",
      "Data columns (total 10 columns):\n",
      "city              71825 non-null object\n",
      "AQI               71825 non-null object\n",
      "PM2.5             71825 non-null object\n",
      "PM10              71825 non-null object\n",
      "SO2               71825 non-null object\n",
      "NO2               71825 non-null object\n",
      "CO                71825 non-null object\n",
      "O3                71825 non-null object\n",
      "main_pollution    71825 non-null object\n",
      "time              71825 non-null object\n",
      "dtypes: object(10)\n",
      "memory usage: 5.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2019-02-04 00:00:00', '2019-02-04 01:00:00',\n",
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       "       '2019-02-04 12:00:00', '2019-02-04 13:00:00',\n",
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       "       '2019-02-04 16:00:00', '2019-02-04 17:00:00',\n",
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       "       '2019-02-04 20:00:00', '2019-02-04 21:00:00',\n",
       "       '2019-02-04 22:00:00', '2019-02-04 23:00:00',\n",
       "       '2019-02-05 00:00:00', '2019-02-05 01:00:00',\n",
       "       '2019-02-05 02:00:00', '2019-02-05 03:00:00',\n",
       "       '2019-02-05 04:00:00', '2019-02-05 05:00:00',\n",
       "       '2019-02-05 06:00:00', '2019-02-05 07:00:00',\n",
       "       '2019-02-05 08:00:00', '2019-02-05 09:00:00',\n",
       "       '2019-02-05 10:00:00', '2019-02-05 11:00:00',\n",
       "       '2019-02-05 12:00:00', '2019-02-05 13:00:00',\n",
       "       '2019-02-05 14:00:00', '2019-02-05 15:00:00',\n",
       "       '2019-02-05 16:00:00', '2019-02-05 17:00:00',\n",
       "       '2019-02-05 18:00:00', '2019-02-05 19:00:00',\n",
       "       '2019-02-05 20:00:00', '2019-02-05 21:00:00',\n",
       "       '2019-02-05 22:00:00', '2019-02-05 23:00:00',\n",
       "       '2019-02-06 00:00:00', '2019-02-06 01:00:00',\n",
       "       '2019-02-06 02:00:00', '2019-02-06 03:00:00',\n",
       "       '2019-02-06 04:00:00', '2019-02-06 05:00:00',\n",
       "       '2019-02-06 06:00:00', '2019-02-06 07:00:00',\n",
       "       '2019-02-06 08:00:00', '2019-02-06 09:00:00',\n",
       "       '2019-02-06 10:00:00', '2019-02-06 11:00:00',\n",
       "       '2019-02-06 12:00:00', '2019-02-06 13:00:00',\n",
       "       '2019-02-06 14:00:00', '2019-02-06 15:00:00',\n",
       "       '2019-02-06 16:00:00', '2019-02-06 17:00:00',\n",
       "       '2019-02-06 18:00:00', '2019-02-06 19:00:00',\n",
       "       '2019-02-06 20:00:00', '2019-02-06 21:00:00',\n",
       "       '2019-02-06 22:00:00', '2019-02-06 23:00:00',\n",
       "       '2019-02-07 00:00:00', '2019-02-07 01:00:00',\n",
       "       '2019-02-07 02:00:00', '2019-02-07 03:00:00',\n",
       "       '2019-02-07 04:00:00', '2019-02-07 05:00:00',\n",
       "       '2019-02-07 06:00:00', '2019-02-07 07:00:00',\n",
       "       '2019-02-07 08:00:00', '2019-02-07 09:00:00',\n",
       "       '2019-02-07 10:00:00', '2019-02-07 11:00:00',\n",
       "       '2019-02-07 12:00:00', '2019-02-07 13:00:00',\n",
       "       '2019-02-07 14:00:00', '2019-02-07 15:00:00',\n",
       "       '2019-02-07 16:00:00', '2019-02-07 17:00:00',\n",
       "       '2019-02-07 18:00:00', '2019-02-07 19:00:00',\n",
       "       '2019-02-07 20:00:00', '2019-02-07 21:00:00',\n",
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       "       '2019-02-08 00:00:00', '2019-02-08 01:00:00',\n",
       "       '2019-02-08 02:00:00', '2019-02-08 03:00:00',\n",
       "       '2019-02-08 04:00:00', '2019-02-08 05:00:00',\n",
       "       '2019-02-08 06:00:00', '2019-02-08 07:00:00',\n",
       "       '2019-02-08 08:00:00', '2019-02-08 09:00:00',\n",
       "       '2019-02-08 10:00:00', '2019-02-08 11:00:00',\n",
       "       '2019-02-08 12:00:00', '2019-02-08 13:00:00',\n",
       "       '2019-02-08 14:00:00', '2019-02-08 15:00:00',\n",
       "       '2019-02-08 16:00:00', '2019-02-08 17:00:00',\n",
       "       '2019-02-08 18:00:00', '2019-02-08 19:00:00',\n",
       "       '2019-02-08 20:00:00', '2019-02-08 21:00:00',\n",
       "       '2019-02-08 22:00:00', '2019-02-08 23:00:00',\n",
       "       '2019-02-09 00:00:00', '2019-02-09 01:00:00',\n",
       "       '2019-02-09 02:00:00', '2019-02-09 03:00:00',\n",
       "       '2019-02-09 04:00:00', '2019-02-09 05:00:00',\n",
       "       '2019-02-09 06:00:00', '2019-02-09 07:00:00',\n",
       "       '2019-02-09 08:00:00', '2019-02-09 09:00:00',\n",
       "       '2019-02-09 10:00:00', '2019-02-09 11:00:00',\n",
       "       '2019-02-09 12:00:00', '2019-02-09 13:00:00',\n",
       "       '2019-02-09 14:00:00', '2019-02-09 15:00:00',\n",
       "       '2019-02-09 16:00:00', '2019-02-09 17:00:00',\n",
       "       '2019-02-09 18:00:00', '2019-02-09 19:00:00',\n",
       "       '2019-02-09 20:00:00', '2019-02-09 21:00:00',\n",
       "       '2019-02-09 22:00:00', '2019-02-09 23:00:00',\n",
       "       '2019-02-10 00:00:00', '2019-02-10 01:00:00',\n",
       "       '2019-02-10 02:00:00', '2019-02-10 03:00:00',\n",
       "       '2019-02-10 04:00:00', '2019-02-10 05:00:00',\n",
       "       '2019-02-10 06:00:00', '2019-02-10 07:00:00',\n",
       "       '2019-02-10 08:00:00', '2019-02-10 09:00:00',\n",
       "       '2019-02-10 10:00:00', '2019-02-10 11:00:00',\n",
       "       '2019-02-10 12:00:00', '2019-02-10 13:00:00',\n",
       "       '2019-02-10 14:00:00', '2019-02-10 15:00:00',\n",
       "       '2019-02-10 16:00:00', '2019-02-10 17:00:00',\n",
       "       '2019-02-10 18:00:00', '2019-02-10 19:00:00',\n",
       "       '2019-02-10 20:00:00', '2019-02-10 21:00:00',\n",
       "       '2019-02-10 22:00:00', '2019-02-10 23:00:00',\n",
       "       '2019-02-11 00:00:00', '2019-02-11 01:00:00',\n",
       "       '2019-02-11 02:00:00', '2019-02-11 03:00:00',\n",
       "       '2019-02-11 04:00:00', '2019-02-11 05:00:00',\n",
       "       '2019-02-11 06:00:00', '2019-02-11 07:00:00',\n",
       "       '2019-02-11 08:00:00', '2019-02-11 09:00:00',\n",
       "       '2019-02-11 10:00:00', '2019-02-11 11:00:00',\n",
       "       '2019-02-11 12:00:00', '2019-02-11 13:00:00',\n",
       "       '2019-02-11 14:00:00', '2019-02-11 15:00:00',\n",
       "       '2019-02-11 16:00:00', '2019-02-11 17:00:00',\n",
       "       '2019-02-11 18:00:00', '2019-02-11 19:00:00',\n",
       "       '2019-02-11 20:00:00', '2019-02-11 21:00:00',\n",
       "       '2019-02-11 22:00:00', '2019-02-11 23:00:00',\n",
       "       '2019-02-12 00:00:00', '2019-02-12 01:00:00',\n",
       "       '2019-02-12 02:00:00', '2019-02-12 03:00:00',\n",
       "       '2019-02-12 04:00:00', '2019-02-12 05:00:00',\n",
       "       '2019-02-12 06:00:00', '2019-02-12 07:00:00',\n",
       "       '2019-02-12 08:00:00', '2019-02-12 09:00:00'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['time'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>AQI</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>main_pollution</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-04</th>\n",
       "      <td>北京市</td>\n",
       "      <td>61.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>61.0</td>\n",
       "      <td>PM10</td>\n",
       "      <td>2019-02-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-04</th>\n",
       "      <td>天津市</td>\n",
       "      <td>104.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>53.0</td>\n",
       "      <td>PM10</td>\n",
       "      <td>2019-02-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-04</th>\n",
       "      <td>石家庄市</td>\n",
       "      <td>125.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>PM10</td>\n",
       "      <td>2019-02-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-04</th>\n",
       "      <td>唐山市</td>\n",
       "      <td>66.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>PM10</td>\n",
       "      <td>2019-02-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-04</th>\n",
       "      <td>秦皇岛市</td>\n",
       "      <td>55.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>48.0</td>\n",
       "      <td>PM10</td>\n",
       "      <td>2019-02-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city    AQI  PM2.5   PM10   SO2   NO2   CO    O3 main_pollution  \\\n",
       "time                                                                          \n",
       "2019-02-04   北京市   61.0   14.0   71.0   3.0  12.0  0.3  61.0           PM10   \n",
       "2019-02-04   天津市  104.0   41.0  157.0   8.0  21.0  0.9  53.0           PM10   \n",
       "2019-02-04  石家庄市  125.0   36.0  200.0  12.0  47.0  0.9  30.0           PM10   \n",
       "2019-02-04   唐山市   66.0   42.0   81.0  22.0  16.0  1.0  52.0           PM10   \n",
       "2019-02-04  秦皇岛市   55.0   25.0   60.0  12.0  22.0  0.9  48.0           PM10   \n",
       "\n",
       "                 time  \n",
       "time                   \n",
       "2019-02-04 2019-02-04  \n",
       "2019-02-04 2019-02-04  \n",
       "2019-02-04 2019-02-04  \n",
       "2019-02-04 2019-02-04  \n",
       "2019-02-04 2019-02-04  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据清洗\n",
    "data['time'] = pd.to_datetime(data['time'])\n",
    "data = data[data['time']<=pd.to_datetime('2019-02-11 23:59:59')]  # 选取2月4日——2月12日的数据\n",
    "data.set_index(data[\"time\"], inplace=True)\n",
    "data = data.replace('—', np.nan)\n",
    "for col in ['AQI', 'PM2.5', 'PM10', 'SO2', 'NO2', 'CO', 'O3']:\n",
    "    data[col] = data[col].astype(float)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 68155 entries, 2019-02-04 00:00:00 to 2019-02-11 23:00:00\n",
      "Data columns (total 10 columns):\n",
      "city              68155 non-null object\n",
      "AQI               67903 non-null float64\n",
      "PM2.5             67815 non-null float64\n",
      "PM10              66437 non-null float64\n",
      "SO2               67832 non-null float64\n",
      "NO2               67852 non-null float64\n",
      "CO                67838 non-null float64\n",
      "O3                67842 non-null float64\n",
      "main_pollution    41176 non-null object\n",
      "time              68155 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(7), object(2)\n",
      "memory usage: 5.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "367"
      ]
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     "execution_count": 8,
     "metadata": {},
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    }
   ],
   "source": [
    "len(data['city'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('data_clean.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 一、数据详情\n",
    "- 共有68155条数据，时间为2月4日到2月11日，每隔1小时收集一次，囊括全国367个城市\n",
    "- 包含字段：城市、空气质量指数、PM2.5指数、PM10指数、SO2指数、NO2指数、CO指数、O3指数、主要污染源、时间\n",
    "\n",
    "#### 二、问题\n",
    "1. 燃放烟花爆竹真的对空气质量有影响吗？\n",
    "2. 烟花爆竹对空气质量的影响体现在哪些指标上？\n",
    "3. 烟花爆竹对空气质量的污染程度有多大？\n",
    "4. 哪些区域污染最严重(轻)？\n",
    "5. 哪些城市属于一秒破功型？\n",
    "6. 除夕中午到初一中午的超标城市个数\n",
    "7. 除夕到初七的超标城市个数\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. 燃放烟花爆竹真的对空气质量有影响吗？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 'time' is both a column name and an index level.\n",
      "Defaulting to column but this will raise an ambiguity error in a future version\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "AQI_total_mean = data[data['time']<=pd.to_datetime('2019-02-06 23:59:59')].groupby(['time'])['AQI'].mean()"
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  },
  {
   "cell_type": "code",
   "execution_count": 11,
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     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "from pyecharts import Line\n",
    "\n",
    "line = Line(\"全国春节期间空气质量指数总体趋势\", \"2019年除夕到初二\", width=800)\n",
    "line.add(\"\", AQI_total_mean.index, np.round(AQI_total_mean.values,0), is_smooth=True, \n",
    "         legend_text_size=18,xaxis_label_textsize=14,yaxis_label_textsize=18,\n",
    "         xaxis_rotate=20, yaxis_min=8, mark_point=[\"max\"])\n",
    "line"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
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   "source": [
    "def city_AQI(data, cities):\n",
    "    line = Line(\"春节期间空气质量指数\", \"2019年除夕到初二\", width=800)\n",
    "    for city in cities:\n",
    "        city_aqi = data[(data['city']==city)&(data['time']<=pd.to_datetime('2019-02-06 23:59:59'))]\n",
    "        line.add(city, city_aqi.index, np.round(city_aqi['AQI'].values,0), is_smooth=True, \n",
    "             legend_text_size=18,xaxis_label_textsize=14,yaxis_label_textsize=18,\n",
    "             xaxis_rotate=20, yaxis_min=8, mark_point=[\"max\"])\n",
    "    return line"
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   "execution_count": 13,
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   "execution_count": 14,
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     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "city_AQI(data=data, cities=['长沙市', '南宁市'])"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. 烟花爆竹对空气质量的影响体现在哪些指标上？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 'time' is both a column name and an index level.\n",
      "Defaulting to column but this will raise an ambiguity error in a future version\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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   ],
   "source": [
    "data_total_idx = data[data['time']<=pd.to_datetime('2019-02-06 23:59:59')].groupby(['time']).mean()"
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  },
  {
   "cell_type": "code",
   "execution_count": 16,
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    {
     "data": {
      "text/plain": [
       "Index(['AQI', 'PM2.5', 'PM10', 'SO2', 'NO2', 'CO', 'O3'], dtype='object')"
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    "data_total_idx.columns"
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
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       "myChart_181fe1bfd7754c9d8c861fe75333480c.setOption(option_181fe1bfd7754c9d8c861fe75333480c);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.line.Line at 0x1107eac18>"
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line = Line(\"全国春节期间空气质量各指标（2019年除夕到初二）\", \"单位:μg/m3(CO为mg/m3)\", width=800)\n",
    "for idx in data_total_idx.columns[1:]:\n",
    "    line.add(idx, data_total_idx.index, np.round(data_total_idx[idx].values,0), is_smooth=True, \n",
    "             legend_text_size=18,xaxis_label_textsize=14,yaxis_label_textsize=18,\n",
    "             xaxis_rotate=20, yaxis_min=8, legend_top=30)\n",
    "line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     },
     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "tianjin_idx = data[(data['city']=='石家庄市')&(data['time']<=pd.to_datetime('2019-02-06 23:59:59'))]\n",
    "line = Line(\"石家庄市春节期间空气质量各指标（2019年除夕到初二）\", \"单位:μg/m3(CO为mg/m3)\", width=800)\n",
    "for idx in tianjin_idx.columns[2:8]:\n",
    "    line.add(idx, tianjin_idx.index, np.round(tianjin_idx[idx].values,0), is_smooth=True, \n",
    "             legend_text_size=18,xaxis_label_textsize=14,yaxis_label_textsize=18,\n",
    "             xaxis_rotate=20, yaxis_min=8, legend_top=30)\n",
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_pol = data.loc[data['time']==pd.to_datetime('2019-02-05 02:00:00'), 'main_pollution'].value_counts()"
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  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "PM2.5         243\n",
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       "PM10,PM2.5     10\n",
       "Name: main_pollution, dtype: int64"
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     "execution_count": 20,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
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       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\"\n",
       "            ],\n",
       "            \"selectedMode\": \"multiple\",\n",
       "            \"show\": true,\n",
       "            \"left\": \"center\",\n",
       "            \"top\": \"top\",\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"textStyle\": {\n",
       "                \"fontSize\": 18,\n",
       "                \"color\": \"#333\"\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"backgroundColor\": \"#fff\",\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"name\": \"\",\n",
       "            \"show\": true,\n",
       "            \"nameLocation\": \"middle\",\n",
       "            \"nameGap\": 25,\n",
       "            \"nameTextStyle\": {\n",
       "                \"fontSize\": 14\n",
       "            },\n",
       "            \"axisLabel\": {\n",
       "                \"interval\": \"auto\",\n",
       "                \"rotate\": 0,\n",
       "                \"margin\": 8,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 18,\n",
       "                    \"color\": \"#000\"\n",
       "                }\n",
       "            },\n",
       "            \"axisTick\": {\n",
       "                \"alignWithLabel\": false\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"position\": null,\n",
       "            \"boundaryGap\": true,\n",
       "            \"min\": null,\n",
       "            \"max\": null,\n",
       "            \"data\": [\n",
       "                \"PM2.5\",\n",
       "                \"PM10\",\n",
       "                \"PM10,PM2.5\"\n",
       "            ],\n",
       "            \"type\": \"category\"\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"name\": \"\",\n",
       "            \"show\": true,\n",
       "            \"nameLocation\": \"middle\",\n",
       "            \"nameGap\": 25,\n",
       "            \"nameTextStyle\": {\n",
       "                \"fontSize\": 14\n",
       "            },\n",
       "            \"axisLabel\": {\n",
       "                \"formatter\": \"{value} \",\n",
       "                \"rotate\": 0,\n",
       "                \"interval\": \"auto\",\n",
       "                \"margin\": 8,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 18,\n",
       "                    \"color\": \"#000\"\n",
       "                }\n",
       "            },\n",
       "            \"axisTick\": {\n",
       "                \"alignWithLabel\": false\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"position\": null,\n",
       "            \"boundaryGap\": true,\n",
       "            \"min\": null,\n",
       "            \"max\": null,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true\n",
       "            },\n",
       "            \"type\": \"value\"\n",
       "        }\n",
       "    ],\n",
       "    \"color\": [\n",
       "        \"#130f40\",\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ]\n",
       "};\n",
       "myChart_dd03eaeaaa464b188df9d7e29eaca4ab.setOption(option_dd03eaeaaa464b188df9d7e29eaca4ab);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.bar.Bar at 0x11363df60>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import Bar\n",
    "\n",
    "bar = Bar(\"全国城市首要污染物\", \"2019年2月5日凌晨2点\", width=600)\n",
    "bar.add(\"\", main_pol.index, main_pol.values, is_stack=True, is_label_show=True,\n",
    "       bar_category_gap='40%', label_color = ['#130f40'], label_text_size=18,\n",
    "       legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)\n",
    "bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. 烟花爆竹对空气质量的污染程度有多大？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data[data['time']<=pd.to_datetime('2019-02-05 23:59:59')]\n",
    "data_AQI_min = data2.groupby('city')['AQI'].min()\n",
    "data_AQI_max = data2.groupby('city')['AQI'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_AQI_times = np.round(data_AQI_max/data_AQI_min, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_AQI_times_top10 = data_AQI_times.nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    });\n",
       "</script>\n",
       "    <div id=\"02d705bb376441d981d5e35fbb5d9ede\" style=\"width:600px;height:400px;\"></div>\n",
       "\n",
       "\n",
       "<script>\n",
       "    require(['echarts'], function(echarts) {\n",
       "        \n",
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       "            \"dataView\": {\n",
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       "    \"series_id\": 748000,\n",
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       "    \"legend\": [\n",
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       "            \"left\": \"center\",\n",
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       "            \"name\": \"\",\n",
       "            \"show\": true,\n",
       "            \"nameLocation\": \"middle\",\n",
       "            \"nameGap\": 25,\n",
       "            \"nameTextStyle\": {\n",
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       "            \"axisLabel\": {\n",
       "                \"interval\": \"auto\",\n",
       "                \"rotate\": 30,\n",
       "                \"margin\": 8,\n",
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       "            \"max\": null,\n",
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       "                \"\\u9521\\u6797\\u90ed\\u52d2\\u76df\",\n",
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       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ]\n",
       "};\n",
       "myChart_02d705bb376441d981d5e35fbb5d9ede.setOption(option_02d705bb376441d981d5e35fbb5d9ede);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.bar.Bar at 0x113645860>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar = Bar(\"全国除夕和春节期间空气质量最高最低比Top10城市\", \"时间：2019年除夕至初一\", width=600)\n",
    "bar.add(\"\", data_AQI_times_top10.index, data_AQI_times_top10.values, is_stack=True, \n",
    "        is_label_show=True,bar_category_gap='40%', label_color = ['#130f40'], label_text_size=18,\n",
    "       legend_text_size=18,xaxis_label_textsize=14,yaxis_label_textsize=18, xaxis_rotate=30)\n",
    "bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "伊春市       22.7\n",
       "鸡西市       22.7\n",
       "锡林郭勒盟     21.4\n",
       "大兴安岭地区    20.4\n",
       "玉树州       19.2\n",
       "盘锦市       17.9\n",
       "葫芦岛市      17.2\n",
       "甘南州       16.7\n",
       "瓦房店市      16.1\n",
       "锦州市       16.1\n",
       "Name: AQI, dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_AQI_times_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts': '/nbextensions/echarts/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "    <div id=\"4b25579e80464bf180e70ce76f526c5a\" style=\"width:800px;height:400px;\"></div>\n",
       "\n",
       "\n",
       "<script>\n",
       "    require(['echarts'], function(echarts) {\n",
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       "var myChart_4b25579e80464bf180e70ce76f526c5a = echarts.init(document.getElementById('4b25579e80464bf180e70ce76f526c5a'), null, {renderer: 'canvas'});\n",
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       "    \"series\": [\n",
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       "            \"symbol\": \"emptyCircle\",\n",
       "            \"symbolSize\": 4,\n",
       "            \"smooth\": true,\n",
       "            \"step\": false,\n",
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       "                32.0,\n",
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     "execution_count": 226,
     "metadata": {},
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   "execution_count": 225,
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       "\n",
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       "</script>\n"
      ],
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       "<pyecharts.charts.line.Line at 0x112a7c2b0>"
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     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_AQI(data=data, cities=['盘锦市','葫芦岛市','瓦房店市','锦州市'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.999, 5.0]    238\n",
       "(5.0, 10.0]      88\n",
       "(10.0, 15.0]     28\n",
       "(15.0, 20.0]      9\n",
       "(20.0, 25.0]      4\n",
       "Name: AQI, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_AQI_times_counts = data_AQI_times.value_counts(bins=[1,5,10,15,20,25])\n",
    "data_AQI_times_counts"
   ]
  },
  {
   "cell_type": "code",
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     "execution_count": 113,
     "metadata": {},
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   ],
   "source": [
    "bar = Bar(\"全国除夕和春节期间空气质量最高最低比的城市数量\", \"时间：2019年除夕至初一\", width=600)\n",
    "times = ['1-5倍','5-10倍','10-15倍','15-20倍','20-25倍']\n",
    "bar.add(\"\", times, data_AQI_times_counts.values, is_stack=True, \n",
    "        is_label_show=True,bar_category_gap='40%', label_color = ['#130f40'], label_text_size=18,\n",
    "       legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18)\n",
    "bar"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4. 哪些区域污染最严重（轻）？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = pd.to_datetime('2019-02-04 20:00:00')\n",
    "end_time = pd.to_datetime('2019-02-05 04:00:00')\n",
    "AQI_by_city = data[(data['time']>=start_time)&(data['time']<=end_time)].groupby('city')['AQI'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "临夏州 coordinates is not found\n",
      "伊犁哈萨克州 coordinates is not found\n",
      "克州 coordinates is not found\n",
      "凉山州 coordinates is not found\n",
      "博州 coordinates is not found\n",
      "大理州 coordinates is not found\n",
      "山南市 coordinates is not found\n",
      "延边州 coordinates is not found\n",
      "德宏州 coordinates is not found\n",
      "怒江州 coordinates is not found\n",
      "恩施州 coordinates is not found\n",
      "文山州 coordinates is not found\n",
      "昌吉州 coordinates is not found\n",
      "昌都市 coordinates is not found\n",
      "普洱市 coordinates is not found\n",
      "林芝市 coordinates is not found\n",
      "果洛州 coordinates is not found\n",
      "楚雄州 coordinates is not found\n",
      "海北州 coordinates is not found\n",
      "海南州 coordinates is not found\n",
      "海西州 coordinates is not found\n",
      "湘西州 coordinates is not found\n",
      "玉树州 coordinates is not found\n",
      "甘南州 coordinates is not found\n",
      "甘孜州 coordinates is not found\n",
      "红河州 coordinates is not found\n",
      "菏泽市 coordinates is not found\n",
      "襄阳市 coordinates is not found\n",
      "西双版纳州 coordinates is not found\n",
      "迪庆州 coordinates is not found\n",
      "阿坝州 coordinates is not found\n",
      "黄南州 coordinates is not found\n",
      "黔东南州 coordinates is not found\n",
      "黔南州 coordinates is not found\n",
      "黔西南州 coordinates is not found\n"
     ]
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       "        \"type\": \"piecewise\",\n",
       "        \"min\": 0,\n",
       "        \"max\": 500,\n",
       "        \"text\": [\n",
       "            \"high\",\n",
       "            \"low\"\n",
       "        ],\n",
       "        \"textStyle\": {\n",
       "            \"color\": \"#fff\"\n",
       "        },\n",
       "        \"inRange\": {\n",
       "            \"color\": [\n",
       "                \"#50a3ba\",\n",
       "                \"#eac763\",\n",
       "                \"#d94e5d\"\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"splitNumber\": 10,\n",
       "        \"dimension\": null,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"left\": \"left\",\n",
       "        \"top\": \"bottom\",\n",
       "        \"showLabel\": true,\n",
       "        \"pieces\": null\n",
       "    }\n",
       "};\n",
       "myChart_c322894d20ba4a579cf0f9700b77c2ce.setOption(option_c322894d20ba4a579cf0f9700b77c2ce);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.geo.Geo at 0x1132418d0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import Geo\n",
    "\n",
    "geo = Geo(\n",
    "    \"全国各城市空气质量指数\",\n",
    "    \"除夕晚上20点至初一凌晨4点平均值\",\n",
    "    title_color=\"#fff\",\n",
    "    title_pos=\"center\",\n",
    "    width=800,\n",
    "    height=500,\n",
    "    background_color=\"#404a59\",\n",
    ")\n",
    "attr, value = AQI_by_city.index, AQI_by_city.values\n",
    "\n",
    "geo.add(\n",
    "    \"\",\n",
    "    attr,\n",
    "    value,\n",
    "    visual_range=[0, 500],\n",
    "    visual_text_color=\"#fff\",\n",
    "    symbol_size=10,\n",
    "    is_visualmap=True,\n",
    "    is_piecewise=True,\n",
    "    visual_split_number=10,\n",
    ")\n",
    "geo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "阿克苏地区    454.333333\n",
       "库尔勒市     436.111111\n",
       "呼和浩特市    394.333333\n",
       "和田地区     357.666667\n",
       "安康市      343.888889\n",
       "白山市      340.333333\n",
       "亳州市      320.333333\n",
       "喀什地区     320.111111\n",
       "包头市      295.666667\n",
       "咸阳市      288.222222\n",
       "Name: AQI, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AQI_by_city.nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</script>\n",
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       "\n",
       "\n",
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       "    require(['echarts'], function(echarts) {\n",
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       "        \"left\": \"95%\",\n",
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     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_AQI(data=data, cities=['呼和浩特市', '包头市', '咸阳市'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "义乌市      12.500000\n",
       "阿勒泰地区    27.111111\n",
       "攀枝花市     27.444444\n",
       "丽江市      28.000000\n",
       "阿里地区     28.222222\n",
       "Name: AQI, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AQI_by_city.nsmallest(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "中山市      41.555556\n",
       "丽江市      28.000000\n",
       "义乌市      12.500000\n",
       "保山市      42.888889\n",
       "嘉峪关市     34.888889\n",
       "塔城地区     33.666667\n",
       "大理州      41.444444\n",
       "安顺市      39.333333\n",
       "惠州市      47.333333\n",
       "拉萨市      36.777778\n",
       "揭阳市      45.222222\n",
       "攀枝花市     27.444444\n",
       "昆明市      48.666667\n",
       "景德镇市     43.444444\n",
       "林芝市      36.333333\n",
       "武威市      47.777778\n",
       "汕尾市      49.555556\n",
       "江门市      42.555556\n",
       "海北州      39.111111\n",
       "海南州      44.333333\n",
       "海口市      41.333333\n",
       "海西州      46.333333\n",
       "深圳市      44.777778\n",
       "珠海市      48.777778\n",
       "甘孜州      43.555556\n",
       "迪庆州      39.111111\n",
       "那曲地区     41.333333\n",
       "金昌市      49.000000\n",
       "阳江市      38.444444\n",
       "阿勒泰地区    27.111111\n",
       "阿坝州      41.222222\n",
       "阿里地区     28.222222\n",
       "Name: AQI, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AQI_by_city[AQI_by_city<=50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5. 哪些城市属于一秒破功型？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "city1 = data.loc[(data['time'] == pd.to_datetime('2019-02-05 02:00:00'))&(data['AQI']<100), 'city']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "data3 = data[(data['time'] >= pd.to_datetime('2019-02-05 18:00:00'))&\n",
    "            (data['time'] <= pd.to_datetime('2019-02-06 23:59:59'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "city2 = data3.loc[data3['AQI']>200, 'city']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['那曲地区', '铜川市', '郑州市', '白城市', '中卫市', '长春市', '吴忠市', '忻州市', '晋城市']"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(set(city1).intersection(set(city2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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     "execution_count": 155,
     "metadata": {},
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   "source": [
    "city_AQI(data=data, cities=['郑州市', '长春市', '忻州市', '晋城市'])"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 6. 除夕中午到初一中午的超标城市个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = pd.to_datetime('2019-02-04 12:00:00')\n",
    "end_time = pd.to_datetime('2019-02-05 12:00:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 'time' is both a column name and an index level.\n",
      "Defaulting to column but this will raise an ambiguity error in a future version\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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   ],
   "source": [
    "city_over_new_year = data.loc[(data['time']>=start_time)&(data['time']<=end_time)&(data['AQI']>100)].groupby('time')['AQI'].count()"
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  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
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       "                197.0\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"normal\": {\n",
       "                    \"show\": false,\n",
       "                    \"position\": \"top\",\n",
       "                    \"textStyle\": {\n",
       "                        \"color\": \"#000\",\n",
       "                        \"fontSize\": 12\n",
       "                    },\n",
       "                    \"formatter\": null\n",
       "                },\n",
       "                \"emphasis\": {\n",
       "                    \"show\": true,\n",
       "                    \"position\": null,\n",
       "                    \"textStyle\": {\n",
       "                        \"color\": \"#fff\",\n",
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       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"normal\": {\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\",\n",
       "                    \"color\": null\n",
       "                }\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0,\n",
       "                \"color\": null\n",
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       "            \"markPoint\": {\n",
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       "            \"markLine\": {\n",
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       "            \"seriesId\": 5289786,\n",
       "            \"xAxisIndex\": 0,\n",
       "            \"yAxisIndex\": 0\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\",\n",
       "                \"\"\n",
       "            ],\n",
       "            \"selectedMode\": \"multiple\",\n",
       "            \"show\": true,\n",
       "            \"left\": \"center\",\n",
       "            \"top\": \"top\",\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"textStyle\": {\n",
       "                \"fontSize\": 18,\n",
       "                \"color\": \"#333\"\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"backgroundColor\": \"#fff\",\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"name\": \"\",\n",
       "            \"show\": true,\n",
       "            \"nameLocation\": \"middle\",\n",
       "            \"nameGap\": 25,\n",
       "            \"nameTextStyle\": {\n",
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       "            },\n",
       "            \"axisLabel\": {\n",
       "                \"interval\": \"auto\",\n",
       "                \"rotate\": 90,\n",
       "                \"margin\": 8,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 14,\n",
       "                    \"color\": \"#000\"\n",
       "                }\n",
       "            },\n",
       "            \"axisTick\": {\n",
       "                \"alignWithLabel\": false\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"position\": null,\n",
       "            \"boundaryGap\": true,\n",
       "            \"min\": null,\n",
       "            \"max\": null,\n",
       "            \"data\": [\n",
       "                \"\\u9664\\u591512\\u70b9\",\n",
       "                \"\\u9664\\u591513\\u70b9\",\n",
       "                \"\\u9664\\u591514\\u70b9\",\n",
       "                \"\\u9664\\u591515\\u70b9\",\n",
       "                \"\\u9664\\u591516\\u70b9\",\n",
       "                \"\\u9664\\u591517\\u70b9\",\n",
       "                \"\\u9664\\u591518\\u70b9\",\n",
       "                \"\\u9664\\u591519\\u70b9\",\n",
       "                \"\\u9664\\u591520\\u70b9\",\n",
       "                \"\\u9664\\u591521\\u70b9\",\n",
       "                \"\\u9664\\u591522\\u70b9\",\n",
       "                \"\\u9664\\u591523\\u70b9\",\n",
       "                \"\\u6625\\u82820\\u70b9\",\n",
       "                \"\\u6625\\u82821\\u70b9\",\n",
       "                \"\\u6625\\u82822\\u70b9\",\n",
       "                \"\\u6625\\u82823\\u70b9\",\n",
       "                \"\\u6625\\u82824\\u70b9\",\n",
       "                \"\\u6625\\u82825\\u70b9\",\n",
       "                \"\\u6625\\u82826\\u70b9\",\n",
       "                \"\\u6625\\u82827\\u70b9\",\n",
       "                \"\\u6625\\u82828\\u70b9\",\n",
       "                \"\\u6625\\u82829\\u70b9\",\n",
       "                \"\\u6625\\u828210\\u70b9\",\n",
       "                \"\\u6625\\u828211\\u70b9\",\n",
       "                \"\\u6625\\u828212\\u70b9\"\n",
       "            ],\n",
       "            \"type\": \"category\"\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"name\": \"\",\n",
       "            \"show\": true,\n",
       "            \"nameLocation\": \"middle\",\n",
       "            \"nameGap\": 25,\n",
       "            \"nameTextStyle\": {\n",
       "                \"fontSize\": 14\n",
       "            },\n",
       "            \"axisLabel\": {\n",
       "                \"formatter\": \"{value} \",\n",
       "                \"rotate\": 0,\n",
       "                \"interval\": \"auto\",\n",
       "                \"margin\": 8,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 18,\n",
       "                    \"color\": \"#000\"\n",
       "                }\n",
       "            },\n",
       "            \"axisTick\": {\n",
       "                \"alignWithLabel\": false\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"position\": null,\n",
       "            \"boundaryGap\": true,\n",
       "            \"min\": null,\n",
       "            \"max\": null,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true\n",
       "            },\n",
       "            \"type\": \"value\"\n",
       "        }\n",
       "    ],\n",
       "    \"color\": [\n",
       "        \"#130f40\",\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ]\n",
       "};\n",
       "myChart_466d671f7fd84be680c636b246ded23a.setOption(option_466d671f7fd84be680c636b246ded23a);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.custom.overlap.Overlap at 0x11324ebe0>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import Overlap\n",
    "\n",
    "bar = Bar(\"全国除夕中午至初一中午空气质量超标城市数量\", \"时间: 2019年  总城市数: 367\", width=1000)\n",
    "new_year_time = ['除夕12点','除夕13点','除夕14点','除夕15点','除夕16点','除夕17点','除夕18点','除夕19点',\n",
    "        '除夕20点','除夕21点','除夕22点','除夕23点','春节0点','春节1点','春节2点','春节3点',\n",
    "        '春节4点','春节5点','春节6点','春节7点','春节8点','春节9点','春节10点','春节11点','春节12点']\n",
    "bar.add(\"\", new_year_time, city_over_new_year.values, is_stack=True, \n",
    "        is_label_show=True,bar_category_gap='40%', label_color = ['#130f40'], label_text_size=18,\n",
    "       legend_text_size=18,xaxis_label_textsize=14,yaxis_label_textsize=18, xaxis_rotate=90)\n",
    "\n",
    "line = Line(\"\", width=1000)\n",
    "line.add(\"\", new_year_time, city_over_new_year.values+5, is_smooth=True)\n",
    "\n",
    "overlap = Overlap(width=1000)\n",
    "overlap.add(bar)\n",
    "overlap.add(line)\n",
    "\n",
    "overlap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 7. 除夕到初七的超标城市个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time\n",
       "2019-02-04    131\n",
       "2019-02-05    209\n",
       "2019-02-06    130\n",
       "2019-02-07     75\n",
       "2019-02-08     23\n",
       "2019-02-09     26\n",
       "2019-02-10     27\n",
       "2019-02-11     83\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_over_count = (data.groupby('city')['AQI'].resample('D').mean()>100).unstack(level=-1).sum()\n",
    "city_over_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "\n",
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       "            \"nameLocation\": \"middle\",\n",
       "            \"nameGap\": 25,\n",
       "            \"nameTextStyle\": {\n",
       "                \"fontSize\": 14\n",
       "            },\n",
       "            \"axisLabel\": {\n",
       "                \"formatter\": \"{value} \",\n",
       "                \"rotate\": 0,\n",
       "                \"interval\": \"auto\",\n",
       "                \"margin\": 8,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 18,\n",
       "                    \"color\": \"#000\"\n",
       "                }\n",
       "            },\n",
       "            \"axisTick\": {\n",
       "                \"alignWithLabel\": false\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"position\": null,\n",
       "            \"boundaryGap\": true,\n",
       "            \"min\": null,\n",
       "            \"max\": null,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true\n",
       "            },\n",
       "            \"type\": \"value\"\n",
       "        }\n",
       "    ],\n",
       "    \"color\": [\n",
       "        \"#130f40\",\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
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       "        \"#749f83\",\n",
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       "        \"#6950a1\",\n",
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       "};\n",
       "myChart_b53848fd982f44bbaeaf258ba5391872.setOption(option_b53848fd982f44bbaeaf258ba5391872);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.custom.overlap.Overlap at 0x1136767f0>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar = Bar(\"全国除夕至初七期间空气质量超标城市数量\", \"时间: 2019年  总城市数: 367\", width=800)\n",
    "date = ['02-04(除夕)','02-05(初一)','02-06(初二)','02-07(初三)',\n",
    "        '02-08(初四)','02-09(初五)','02-10(初六)','02-11(初七)']\n",
    "bar.add(\"\", date, city_over_count.values, is_stack=True, \n",
    "        is_label_show=True,bar_category_gap='40%', label_color = ['#130f40'], label_text_size=18,\n",
    "       legend_text_size=18,xaxis_label_textsize=18,yaxis_label_textsize=18, xaxis_rotate=20)\n",
    "\n",
    "line = Line(\"\", width=800)\n",
    "line.add(\"\", date, city_over_count.values+5, is_smooth=True)\n",
    "\n",
    "overlap = Overlap(width=700)\n",
    "overlap.add(bar)\n",
    "overlap.add(line)\n",
    "\n",
    "overlap"
   ]
  }
 ],
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